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Type :thesis
Subject :QA Mathematics
Main Author :Altaha, Mohamed Aktham Ahmed
Title :Malaysian sign language recognition framework based on sensory glove
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2019
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Guest :Click to view PDF file

Abstract : Universiti Pendidikan Sultan Idris
The purpose of this study was to propose a low-cost and real-time recognition system using a  sensory glove, which has 17 sensors with 65 channels to capture static sign data of the Malaysian  sign language (MSL). The study uses an experimental design. Five participants  well-known  MSL   were  chosen  to  perform  75  gestures  throughout  wear sensory glove. This research was carried  out in six phases as follows: Phase I involved a review of literature via a systematic review  approach to identify the relevant set of articles that helped formulate the research questions.  Phase II focused on the analysis of hand anatomy, hand kinematic, and hand gestures to help  understand the nature of MSL and to define the glove requirements. In Phase III, DataGlove was  designed and developed based on the glove requirements to help optimize the best functions of the  glove. Phase IV involved the pre-processing, feature extraction, and classification of the data  collected from the proposed DataGlove and identified gestures of MSL. A new vision and sensor-based  MSL datasets were collected in Phase V. Phase VI focused on the evaluation and validation process  across different development stages. The error rate was used to check system performance. Also, a  3D printed humanoid arm was used to validate the sensor mounted on the glove. The results of data  analysis showed 37 common  patterns  with  similar  hand  gestures  in  MSL.  Furthermore,  the   design  of DataGlove based on MSL analysis was effective in capturing a wide range of gestures with  a recognition accuracy of 99%, 96%, and 93.4% for numbers, alphabet letters, and words,   respectively.  In  conclusion,  the  research  findings  suggest  that  37  group's gestures of MSL  can increase the recognition accuracy of MSL hand gestures to bridge the  gap  between  people   with  hearing  impairments  and  ordinary  people.  For  future research,   a   more    comprehensive   analysis   of   the   MSL   recognition   system   is recommended.  

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